{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a5c7de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d98ad331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品</th>\n",
       "      <th>2021销量</th>\n",
       "      <th>2022销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>口红</td>\n",
       "      <td>3568</td>\n",
       "      <td>2569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>面膜</td>\n",
       "      <td>4135</td>\n",
       "      <td>3241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>隔离</td>\n",
       "      <td>4436</td>\n",
       "      <td>2965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>防晒</td>\n",
       "      <td>4106</td>\n",
       "      <td>3209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>精华</td>\n",
       "      <td>4936</td>\n",
       "      <td>3541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   商品  2021销量  2022销量\n",
       "0  口红    3568    2569\n",
       "1  面膜    4135    3241\n",
       "2  隔离    4436    2965\n",
       "3  防晒    4106    3209\n",
       "4  精华    4936    3541"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_excel('第二章 图表(前15).xlsx',sheet_name='9 对比柱形图',usecols=\"B,C,D\",skiprows=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0966efb5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "375350d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = data['商品']\n",
    "values1 = data['2021销量']\n",
    "values2 = data['2022销量']\n",
    "\n",
    "# 设置柱状图的宽度和偏移量\n",
    "bar_width = 0.25        # 宽度\n",
    "bar_offset = bar_width  # 偏移量\n",
    "\n",
    "# 设置x轴的位置\n",
    "index = np.arange(len(categories))\n",
    "\n",
    "# 创建图形和轴\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# 绘制分组柱状图\n",
    "bar1 = ax.bar(index - bar_offset, values1, bar_width, label='2021', color='skyblue', bottom=0)\n",
    "bar2 = ax.bar(index, values2, bar_width, label='2022', color='lightgreen', bottom=0)\n",
    "\n",
    "# 添加图例\n",
    "ax.legend()\n",
    "\n",
    "# 设置x轴和y轴的标签\n",
    "ax.set_title('2022年商品对比去年销售情况')\n",
    "\n",
    "# 设置x轴的刻度\n",
    "ax.set_xticks(index - bar_offset + bar_width / 2)  # 将刻度设置在每组的中间位置\n",
    "ax.set_xticklabels(categories)\n",
    "\n",
    "for a,b in zip(index - bar_offset,values1):   #柱子上的数字显示\n",
    "    plt.text(a,b,'%.2f'%b,ha='center',va='bottom',fontsize=9);\n",
    "\n",
    "for a,b in zip(index - bar_offset + bar_width ,values2):   #柱子上的数字显示\n",
    "    plt.text(a,b,'%.2f'%b,ha='center',va='bottom',fontsize=9);\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
